Analysis of Noise Sensitivity of Different ECG Detection Algorithms

This paper presents an analysis of noise sensitivities of different detection algorithms for electrocardiogram (ECG) taken from MIT-BIH arrhythmia database. Seven methods used in this paper are based on derivatives, digital filters (DF), neural network (NN) and wavelet transform (WT). The raw ECG is corrupted with 5 different types of synthesized noise, namely, power line interference, base line drift due to respiration, abrupt baseline shift, electromyogram (EMG) interference and a composite noise made from other types. A total of 315 data sets are constructed from 15 raw data sets for each type of noise adding 0%, 25%, 50%, 75% and 100% noise levels. The application of the methods to detect QRS complexes of a total of 33,774 beats of ECG shows that none of the algorithms are able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on NN and WT show better performance considering all noise types and the two algorithms perform similarly. The result of this study will help to develop a more robust ECG detector and this will make ECG interpretation system more effective.DOI:http://dx.doi.org/10.11591/ijece.v3i3.251

Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network

Electroencephalogram (EEG) signals have great importance in the area of brain-computer interface (BCI) which has diverse applications ranging from medicine to entertainment. BCI acquires brain signals, extracts informative features and generates control signals from the knowledge of these features for functioning of external devices. The objective of this work is twofold. Firstly, to extract suitable features related to hand movements and secondly, to discriminate the left and right hand movements signals finding effective classifier. This work is a continuation of our previous study where beta band was found compatible for hand movement analysis. The discrete wavelet transform (DWT) has been used to separate beta band of the EEG signal in order to extract features.  The performance of a probabilistic neural network (PNN) is investigated to find better classifier of left and right hand movements EEG signals and compared with classical back propagation based neural network. The obtained results shows that PNN (99.1%) has better classification rate than the BP (88.9%). The results of this study are expected to be helpful in brain computer interfacing for hand movements related bio-rehabilitation applications

Study on viscosity induced contrast in ultrasound color flow imaging of carotid atherosclerosis

Efficient imaging of blood flow disturbances resulted from carotid atherosclerosis plays a vital role clinically to predict brain stroke risk. Carotid atherosclerosis and its development is closely linked with raised blood viscosity. Therefore, study of viscosity changing hemodynamic effect has importance and it might be useful for improved examination of carotid atherosclerosis incorporating the viscosity induced contrast in conventional ultrasound imaging. This work considered the design of realistic models of atherosclerotic carotid artery of different stages and solved to compute the hemodisturbances using computational fluid dynamics (CFD) by finite element method (FEM) to investigate viscosity changes effect. Ultrasound color flow image of velocities of blood have been constructed using phase shift information estimated with autocorrelation of Hilbert transformed simulated backscattered radiofrequency (RF) signals from moving blood particles. The simulated ultrasound images have been compared with CFD simulation images and identified a good match between them. The atherosclerosis stages of the models have been investigated from the estimated velocity data. It has been observed that the blood velocities increase noticeably in carotid atherosclerotic growths and velocity distribution changes with viscosity variations. It is also found importantly that the viscosity induced contrast associated to atherosclerosis is detectable in ultrasound color flow imaging. The findings of this work might be useful for better investigation of carotid atherosclerosis as well as prediction of its progression to reduce the stroke risk

Street Object Detection from Synthesized and Processed Semantic Image: A Deep Learning Based Study

Abstract Semantic image synthesis plays an important role in the development of Advanced Driver Assistance System (ADAS). Street objects detection might be erroneous during raining or when images from vehicle’s camera are blurred, which can cause serious accidents. Therefore, automatic and accurate street object detection is a demanding research scope. In this paper, a deep learning based framework is proposed and investigated for street object detection from synthesized and processed semantic image. Firstly, a Conditional Generative Adversarial Network (CGAN) has been used to create the realistic image. The brightness of the CGAN generated image has been increased using neural style transfer method. Furthermore, Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) based image enhancement concept has been used to improve the resolution of style-transferred images. These processed images exhibit better clarity and high fidelity which is impactful in the performance improvement of object detector. Finally, the synthesized and processed images were used as input in a Region-based Convolutional Neural Network (Faster R-CNN) and a MobileNet Single Shot Detector (MobileNetSSDv2) model separately for object detection. The widely used Cityscape dataset is used to investigate the performance of the proposed framework. The results analysis shows that the used synthesized and processed input improves the performance of the detectors than the unprocessed counterpart. A comparison of the proposed detection framework with related state of the art techniques is also found satisfactory with a mean average precision (mAP) around 32.6%, whereas most of the cases, mAPs are reported in the range of 20–28% for this particular dataset

Study on Compression Induced Contrast in X-ray Mammograms Using Breast Mimicking Phantoms

X-ray mammography is commonly used to scan cancer or tumors in breast using low dose x-rays. But mammograms suffer from low contrast problem. The breast is compressed in mammography to reduce x-ray scattering effects. As tumors are stiffer than normal tissues, they undergo smaller deformation under compression. Therefore, image intensity at tumor region may change less than the background tissues. In this study, we try to find out compression induced contrast from multiple mammographic images of tumorous breast phantoms taken with different compressions. This is an extended work of our previous simulation study with experiment and more analysis. We have used FEM models for synthetic phantom and constructed a phantom using agar and n-propanol for simulation and experiment. The x-ray images of deformed phantoms have been obtained under three compression steps and a non-rigid registration technique has been applied to register these images. It is noticeably observed that the image intensity changes at tumor are less than those at surrounding which induce a detectable contrast. Addition of this compression induced contrast to the simulated and experimental images has improved their original contrast by a factor of about 1.

DeepRoadNet: A deep residual based segmentation network for road map detection from remote aerial image

Abstract The extraction of road networks is a critical activity in contemporary transportation networks. Deep neural networks have recently demonstrated excellent performance in the field of road segmentation. However, most of the convolutional neural network (CNN) based architectures could not verify their effectiveness in remote sensing images due to a smaller ratio of the targeted pixels, simple design, and fewer layers. In this study, a practical approach is assessed for road segmentation. The investigation was begun with basic encoder–decoder based segmentation models. Different state‐of‐the‐art segmentation models like U‐Net, V‐Net, ResUNet and SegNet were used for road network detection experiments in this research. A robust model named DeepRoadNet, a more complicated alternative, is proposed by utilizing a pre‐trained EfficientNetB7 architecture in the encoder and residual blocks as the decoder which mostly resembles the U‐Net segmentation process. The proposed model has been trained, validated as well as tested using the high‐resolution aerial image datasets and yielded good segmentation results with a mean intersection over union (mIoU) of 76%, a mean dice coefficient (mDC) of 73.18%, and an accuracy of 97.64% using Massachusetts road dataset. The proposed DeepRoadNet architecture overcomes the issues of lower mIoU, lower mDC, limited flexibility and interpretability already faced by existing models in the road segmentation field. The code is available at https://github.com/Imteaz1998/DeepRoadNet